UNITER: UNiversal Image-TExt Representation Learning

Yen-Chun Chen; Linjie Li; Licheng Yu; Ahmed El Kholy; Faisal Ahmed; Zhe Gan; Yu Cheng; Jingjing Liu

doi:10.1007/978-3-030-58577-8_7

Abstract

Patients with vulvar (VC) and vaginal (VaC) cancer are often frail and should be prescreened before a time-consuming comprehensive geriatric assessment (CGA). This study assessed the impact of the preoperatively determined frailty status with the G8 geriatric screening tool (G8) and comorbidity assessment on the outcome of patients with VC/VaC. We conducted an observational study with prospective data collection of patients aged ≥ 60 undergoing surgery for VC and VAC from 05/2020 to 01/2025. Patients were assessed with the G8 tool, age-adjusted Charlson-Comorbidity Index and the Lee-index. Positive G8 results led to CGA-based testing and, if indicated, geriatric consultation. Cox regression, Kaplan–Meier curves and propensity score matching (PSM) were used to analyze the predictive validity of the G8. The G8 can be easily implemented in the clinical routine to identify VC and VaC patients with a reduced 2-year OS who may benefit from CGA. The online version contains supplementary material available at 10.1007/s00432-025-06378-5.

Keywords

Computer scienceArtificial intelligenceMasking (illustration)Natural language processingLanguage modelClosed captioningImage (mathematics)Question answeringPattern recognition (psychology)

Affiliated Institutions

Microsoft (United States) US

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Publication Info

Year: 2020
Type: book-chapter
Pages: 104-120
Citations: 1709
Access: Closed

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APA Style

                            
                                    Yen-Chun Chen, 
                                
                                    Linjie Li, 
                                
                                    Licheng Yu
                                
                                et al.
                            
                            (2020). 
                            UNITER: UNiversal Image-TExt Representation Learning. 
                            Lecture notes in computer science
                            
                            , 104-120.
                            https://doi.org/10.1007/978-3-030-58577-8_7

Identifiers

DOI: 10.1007/978-3-030-58577-8_7
PMID: 41361593
PMCID: PMC12686312

Data Quality

Data completeness: 77%